Wanikani Python API Docs | dltHub

WaniKani is a spaced-repetition web service for learning Japanese kanji and vocabulary that exposes a REST API (WaniKani API v2) to access user progress and reference data. The REST API base URL is https://api.wanikani.com/v2 and all requests require a Bearer token for authentication.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Wanikani data in under 10 minutes.

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What data can I load from Wanikani?

Here are some of the endpoints you can load from Wanikani:

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How do I authenticate with the Wanikani API?

Provide your personal API token in the Authorization HTTP header as: Authorization: Bearer <api_token>. Requests must be made over HTTPS. Optionally include Wanikani-Revision header to request a specific revision (default 20170710).

1. Get your credentials

1. Sign in to your WaniKani account.
2. Go to Settings → API Tokens (or open https://www.wanikani.com/settings/personal_access_tokens).
3. Create a v2 personal access token or copy an existing one.
4. Store the token securely; use it as the Bearer token in requests.

2. Add them to .dlt/secrets.toml

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[sources.wanikani_source]
api_key = "your_wanikani_api_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Wanikani API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python wanikani_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline wanikani_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset wanikani_data
The duckdb destination used duckdb:/wanikani.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline wanikani_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads subjects and summary from the Wanikani API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def wanikani_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.wanikani.com/v2",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "subjects", "endpoint": {"path": "subjects", "data_selector": "data"}},
            {"name": "summary", "endpoint": {"path": "summary", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="wanikani_pipeline",
        destination="duckdb",
        dataset_name="wanikani_data",
    )
    load_info = pipeline.run(wanikani_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("wanikani_pipeline").dataset()
sessions_df = data.subjects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM wanikani_data.subjects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("wanikani_pipeline").dataset()
data.subjects.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Wanikani data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you get HTTP 401 Unauthorized, ensure the Authorization header is present and correct: Authorization: Bearer <api_token>. Verify the token on https://www.wanikani.com/settings/personal_access_tokens and that you are using HTTPS. Missing or malformed header will return 401 and a JSON error like {"error":"Unauthorized. Nice try.","code":401}.

Rate limits and 429 responses

WaniKani enforces rate limits. Excessive requests return HTTP 429 Too Many Requests. Back off and retry after a pause. Use caching, updated_after filters, and per-page limits to reduce requests.

Pagination

Collection responses use cursor-based pagination; list responses are objects with a data array and pages.next_url/pages.previous_url. Use pages.next_url or the page_after_id parameter to iterate. The data array is in the top-level "data" key for collection endpoints.

Deprecated endpoints and 404s

Some endpoints (e.g., reviews list) are deprecated and may return empty arrays or 404s. A 404 response body may be {"error":"Not found","code":404}.

Common error responses

WaniKani uses standard HTTP codes: 401 Unauthorized, 403 Forbidden, 404 Not Found, 409 Conflict, 422 Unprocessable Entity (with details), 429 Too Many Requests, 500/503 server errors. Error bodies follow {"error": , "code": }.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack